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Abstract
BackgroundIn South Korea, there is currently no syndromic surveillance system using internet search data, including Google Flu Trends. The purpose of this study was to investigate the correlation between national influenza surveillance data and Google Trends in South Korea.MethodsOur study was based on a publicly available search engine database, Google Trends, using 12 influenza-related queries, from September 9, 2007 to September 8, 2012. National surveillance data were obtained from the Korea Centers for Disease Control and Prevention (KCDC) influenza-like illness (ILI) and virologic surveillance system. Pearson's correlation coefficients were calculated to compare the national surveillance and the Google Trends data for the overall period and for 5 influenza seasons.ResultsThe correlation coefficient between the KCDC ILI and virologic surveillance data was 0.72 (p<0.05). The highest correlation was between the Google Trends query of H1N1 and the ILI data, with a correlation coefficient of 0.53 (p<0.05), for the overall study period. When compared with the KCDC virologic data, the Google Trends query of bird flu had the highest correlation with a correlation coefficient of 0.93 (p<0.05) in the 2010-11 season. The following queries showed a statistically significant correlation coefficient compared with ILI data for three consecutive seasons: Tamiflu (r = 0.59, 0.86, 0.90, p<0.05), new flu (r = 0.64, 0.43, 0.70, p<0.05) and flu (r = 0.68, 0.43, 0.77, p<0.05).ConclusionsIn our study, we found that the Google Trends for certain queries using the survey on influenza correlated with national surveillance data in South Korea. The results of this study showed that Google Trends in the Korean language can be used as complementary data for influenza surveillance but was insufficient for the use of predictive models, such as Google Flu Trends.
Highlights
Syndromic surveillance is defined a dynamic process of collecting real-time or near real-time data about symptom clusters that are suggestive of a biological disease outbreak[1,2]
The 2009 (H1N1) influenza pandemic highlighted the need for a syndromic surveillance system to assist the policy and planning for effective health system responses
The purpose of this study was to investigate the correlation between national influenza surveillance and Google Trends (GT) data
Summary
Syndromic surveillance is defined a dynamic process of collecting real-time or near real-time data about symptom clusters that are suggestive of a biological disease outbreak[1,2]. Conventional surveillance for influenza is recommended to monitor influenza-like illness (ILI) and influenza virus infections Such surveillance involves the collection and analysis of data from sentinel clinics and laboratories. Because this mode of surveillance is dependent on case reporting and medical records to track disease activity, time delays in the reporting and case confirmation can prevent early detection of outbreaks or increases in influenza. Alternative data sources include school absenteeism[6,7,8], over-the-counter pharmaceutical sales[9,10,11], and ambulance dispatch data[12,13] Using those data, disease clusters may be detected earlier than by conventional surveillance.
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